{
  "data": {
    "a": {
      "slug": "mimo-v2-6-distill-qwen-9b",
      "name": "MiMo-V2.6 Distill Qwen 9B",
      "vendor": "Xiaomi (MiMo team)",
      "vendorUrl": "https://mimo.xiaomi.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiMo-V2.6 Distill Qwen 9B is Xiaomi's open-weight 9B agentic model, Qwen3.5-9B fine-tuned on data generated by MiMo-V2.6 for coding, agent, visual coding and cybersecurity tasks, released in BF16 under MIT as a starting point for RL research.",
      "url": "https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b",
      "markdownUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-distill-qwen-9b.json",
      "dns": "mimo-v2-6-distill-qwen-9b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B",
      "license": "MIT (metadata only, no LICENSE file); built on Qwen3.5-9B under Apache 2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under MIT on the card and Apache 2.0 for the Qwen3.5-9B base. Xiaomi's MiMo API serves the larger MiMo-V2.6 models, not this one. Running it yourself costs the hardware, 18.8 GB for the BF16 files (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-03"
      },
      "docsUrl": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "reasoning",
        "tool-calling",
        "derivative",
        "distillation",
        "mit",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-09-21",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 37,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 70,
          "reach": 80,
          "risk": 65,
          "upkeep": 40
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "A 9B cousin tuned on MiMo-V2.6's agent work for coding, terminal tasks and visual coding, released as a base for RL research. Ollama's library build needs about 8 GB at 4-bit, while Xiaomi ships only BF16. The main risks are no safety results while a seventh of its tuning is offensive security, and nobody at Xiaomi answering reports.",
        "bestFor": "Researchers and agents that want a small coding and terminal-agent model to fine-tune further on a 24 GB GPU, or to run at 4-bit through Ollama in about 8 GB.",
        "strengths": [
          "Runs on Qwen3.5-9B's architecture, so released vLLM, SGLang, Transformers and llama.cpp load it, and Ollama's library carries it at 4-bit and 8-bit",
          "MIT over an Apache 2.0 base, no gate, no account and no use policy",
          "Safetensors only in Xiaomi's own organisation, with a checksum on every file",
          "The fine-tuning mix is published by domain with token counts, and a technical report covers it",
          "Several tool calls a turn, thinking switched per request and 262,144 tokens of context from the base"
        ],
        "weaknesses": [
          "Only BF16 from Xiaomi, about 23 GB to run; the smaller builds are Ollama's",
          "No safety results or agent guidance beyond general cautions, with 11.0B tokens of offensive-security tuning",
          "No reply from Xiaomi to any of its 14 discussions, including template and tool-call reports",
          "No LICENSE file and no copy of the base's Apache 2.0 text, and the card has no limitations, context, languages or cut-off",
          "The tool format and parser aren't documented on the card"
        ],
        "agentNotes": [
          "Quickest start is `ollama run mimo-v2.6:9b`, Ollama's 6.6 GB q4_K_M build with text and image input",
          "Serve it as the card does, `sglang serve --model-path XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B --reasoning-parser mimo`, and add `--tool-call-parser mimo` for parsed tool calls",
          "SGLang's mimo reasoning parser expects thinking to be asked for, so pass `chat_template_kwargs` with `enable_thinking` true, as the card does, and read `reasoning_content`",
          "Calls come back as function and parameter blocks inside tool_call, the same shape vLLM's and SGLang's qwen3_coder parsers read",
          "The generation config samples at temperature 0.6, top_k 20 and top_p 0.95",
          "Images and video go in through Qwen3.5's processor; the template's audio tokens have no audio encoder behind them",
          "The MTP head isn't in the files, so skip MTP speculative decoding even though config.json lists a layer",
          "It's an SFT checkpoint meant for further RL, and discussions report runaway generation on llama.cpp, so cap max_tokens",
          "If you redistribute the weights, include the Apache 2.0 text of Qwen3.5-9B, which section 4(a) asks for and the repository lacks"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 70,
          "reach": 80,
          "risk": 65,
          "upkeep": 40
        },
        "provenanceScore": 47,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "lastMoved": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "sglang serve --model-path XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B \\\n  --reasoning-parser mimo --tool-call-parser mimo --host 0.0.0.0 --port 30000\n# or, with llama.cpp and llama.cpp's own GGUF:\nllama-server -hf ggml-org/MiMo-V2.6-Distill-Qwen-9B-GGUF:Q8_0 --jinja",
        "http": "curl http://localhost:30000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"chat_template_kwargs\": {\"enable_thinking\": true}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/mimo-v2-6-distill-qwen-9b"
      },
      "sameCompany": [
        "mimo-v2-6-flash-rl"
      ],
      "weights": {
        "trainedBy": "Xiaomi (MiMo team)",
        "builtOn": "Qwen/Qwen3.5-9B (supervised fine-tune on data generated by MiMo-V2.6; base Apache 2.0, declared in the card's metadata)",
        "params": "9.41B",
        "context": 262144,
        "goodFor": "A 9B agentic model for coding, terminal and tool tasks, with images in, meant as a starting point for RL fine-tuning.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "ollama mimo-v2.6:9b-q4_K_M",
            "url": "https://ollama.com/library/mimo-v2.6:9b-q4_K_M",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 6.6,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Size as Ollama's tags page shows it (the default 9b tag is listed at 6.6 to 8.7 GB across formats), text and image input, 256K context. Ollama also lists 9b-q8_0 (10 GB), 9b-mxfp8 (11 GB), 9b-nvfp4 (8.7 GB) and BF16 GGUF and MLX builds (19 GB)"
          },
          {
            "name": "ggml-org MiMo-V2.6-Distill-Qwen-9B-GGUF (Q8_0)",
            "url": "https://huggingface.co/ggml-org/MiMo-V2.6-Distill-Qwen-9B-GGUF",
            "who": "llama.cpp",
            "precision": "q8_0",
            "sizeGB": 10.15,
            "memoryGB": 16,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation. 9.53 GB of Q8_0 weights plus the 0.62 GB Q8_0 vision projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "MiMo-V2.6-Distill-Qwen-9B",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 18.85,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ],
            "note": "Xiaomi's only build"
          }
        ],
        "run": "sglang serve --model-path XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B --reasoning-parser mimo --tool-call-parser mimo --context-length 131072 --port 30000",
        "runBuild": "MiMo-V2.6-Distill-Qwen-9B",
        "checked": "2026-11-03",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Xiaomi (MiMo)",
        "family": "MiMo-V2 (V2, V2.5, V2.6)",
        "url": "https://www.anchorterminal.com/open-weights/#xiaomi",
        "others": [
          {
            "name": "MiMo-V2.6-Pro-MOPD",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-MOPD",
            "params": "1024.22B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.6-Flash-MOPD",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-MOPD",
            "params": "310.76B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.6-Pro-RL",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL",
            "params": "1024.22B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.6-Flash-RL",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL",
            "params": "310.76B (Hub safetensors count; the name states none)",
            "listing": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl"
          },
          {
            "name": "MiMo-V2.5-Pro-Base",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro-Base",
            "params": "1023.18B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.5-Pro",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro",
            "params": "1023.18B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.5-Base",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Base",
            "params": "310.76B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.5",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5",
            "params": "310.76B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.5-ASR",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-ASR",
            "params": "7.62B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2-Flash-Base",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash-Base",
            "params": "309.77B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2-Flash",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash",
            "params": "309.77B (Hub safetensors count; the name states none)"
          }
        ],
        "olderCount": 12,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Xiaomi (the card and report name Xiaomi MiMo; no LICENSE file names a copyright holder)",
        "domain": "mimo.xiaomi.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-11-03",
        "notes": [
          "The Hugging Face organisation XiaomiMiMo isn't verified. mimo.xiaomi.com links to it, and the MiMo-V2.6 technical report links this repository.",
          "Nothing is hosted for this model, so the licence stands in for terms of service and no privacy policy is scored.",
          "No changelog. The six commits are the upload on 21 September 2026, a README update on 22 September and two README updates on 28 October.",
          "mimo.xiaomi.com has no security.txt (404) and www.mi.com's returns 403. Xiaomi runs a company security centre at sec.xiaomi.com, which no MiMo page links."
        ],
        "score": 47
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b.json",
      "live": {
        "slug": "mimo-v2-6-distill-qwen-9b",
        "securityTxt": {
          "url": "https://mimo.xiaomi.com/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:07:46.192470354Z"
        },
        "domain": {
          "domain": "xiaomi.com",
          "registered": "2003-07-22",
          "source": "https://rdap.verisign.com/com/v1/domain/xiaomi.com",
          "checkedAt": "2026-11-08T15:08:19.964915329Z"
        },
        "updatedAt": "2026-11-10T16:07:46.192470354Z"
      }
    },
    "answer": "MiMo-V2.6 Distill Qwen 9B scores 61 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 3 of 4 scored categories.",
    "b": {
      "slug": "sensenova-loop-9b-preview",
      "name": "SenseNova-Loop-9B-Preview",
      "vendor": "SenseTime (SenseNova)",
      "vendorUrl": "https://www.sensetime.com/en",
      "kind": "weights",
      "category": "open-weights",
      "summary": "SenseNova-Loop-9B-Preview is SenseTime's open-weight multimodal model of about 9B parameters. It runs 24 of its 32 decoder layers twice, with text and images in, tool calling, a thinking switch and a 256K context. Apache 2.0. No released runtime loads it.",
      "url": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview",
      "markdownUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.json",
      "dns": "sensenova-loop-9b-preview.grade.anchorterminal.com",
      "repo": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "sensenova/SenseNova-Loop-9B-Preview"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself costs the hardware: the files are 38.1 GB because most weights are stored in 32-bit floats, and the card's example loads them as BF16 (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://github.com/OpenSenseNova/SenseNova-Loop",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "multimodal",
        "vision",
        "looped-transformer",
        "tool-calling",
        "reasoning",
        "long-context",
        "preview",
        "apache-2.0",
        "safetensors",
        "custom-code",
        "self-hosted"
      ],
      "lastRelease": "2026-11-02",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 46.8,
        "grade": "D",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 110,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 40,
          "risk": 50,
          "upkeep": 44
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet.",
        "bestFor": "Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today",
        "strengths": [
          "Apache 2.0, ungated",
          "A 256K training context, stated on the card",
          "Thinking can be switched per request",
          "The architecture and parameter counts are documented",
          "Changed in the last ten days"
        ],
        "weaknesses": [
          "No released runtime runs it. vLLM and SGLang need unmerged pull requests",
          "Loading it runs Python from the repository",
          "The files are 38.1 GB, mostly F32, though the card says BF16",
          "No quantised build",
          "No tool-calling documentation beyond the template",
          "No limitations, safety results or data sources, and the technical report isn't out",
          "None of 5 questions answered"
        ],
        "agentNotes": [
          "Read modeling_sensenova_loop.py before you load it. trust_remote_code runs it",
          "Pin transformers 5.18.0, the version the card checked",
          "Load with dtype=torch.bfloat16 as the card does, or the 38 GB of F32 weights stay at full size",
          "Pass enable_thinking=False to apply_chat_template for a direct answer",
          "Tool calls come back as tool_call blocks with function and parameter tags. Parse them yourself, since no runtime ships a parser",
          "Wait for the vLLM or SGLang pull request to land in a release before putting it behind an API"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 46.8
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 40,
          "risk": 50,
          "upkeep": 44
        },
        "provenanceScore": 51,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "pip install \"transformers==5.18.0\" accelerate safetensors\n# then AutoModelForCausalLM.from_pretrained(\"sensenova/SenseNova-Loop-9B-Preview\", trust_remote_code=True, dtype=torch.bfloat16)\n# trust_remote_code runs modeling_sensenova_loop.py from the repository. Read it first",
        "http": "# No released server runs it. The card's vLLM and SGLang commands build from open pull requests,\n# and its LightLLM example serves an OpenAI-compatible API at http://localhost:8000/v1 on four GPUs"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/sensenova-loop-9b-preview"
      },
      "weights": {
        "trainedBy": "SenseTime (SenseNova)",
        "params": "9B",
        "context": 262144,
        "goodFor": "Trying a looped transformer for long-context, tool-using work, if you can run the repository's own code on a large GPU.",
        "runtimes": [],
        "builds": [
          {
            "name": "SenseNova-Loop-9B-Preview (safetensors, mostly F32)",
            "url": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview/tree/main",
            "who": "publisher",
            "precision": "f32",
            "sizeGB": 38.14,
            "memoryGB": 48,
            "format": "safetensors",
            "note": "Eight shards and the MTP head. Stored mostly as 32-bit floats; the card's example loads it as BF16, about half the size in memory"
          }
        ],
        "run": "python -c \"from transformers import AutoModelForCausalLM; AutoModelForCausalLM.from_pretrained('sensenova/SenseNova-Loop-9B-Preview', trust_remote_code=True)\"",
        "runBuild": "SenseNova-Loop-9B-Preview (safetensors, mostly F32)",
        "checked": "2026-11-10",
        "minMemoryGB": 48
      },
      "family": {
        "lab": "SenseTime (SenseNova)",
        "family": "SenseNova-Loop",
        "url": "https://www.anchorterminal.com/open-weights/#sensenova"
      },
      "area": "models",
      "provenance": {
        "legalEntity": "SenseTime, listed on the Stock Exchange of Hong Kong (00020) per its investor page. SenseNova is its foundation model series, per its site",
        "domain": "sensetime.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://github.com/OpenSenseNova/SenseNova-Loop/blob/main/LICENSE",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The card names SenseNova only. SenseTime's site presents SenseNova as its foundation model series, and that is the tie between the two; the site wasn't read for a link to this repository.",
          "The domain's registration date wasn't looked up.",
          "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.",
          "www.sensetime.com has no security.txt (404 on 10 November 2026). Its about page gives a security contact.",
          "The changelog is the commit history, 39 commits from 28 October to 2 November 2026."
        ],
        "score": 51
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.json"
    },
    "edges": [
      {
        "by": 40,
        "category": "reach",
        "leads": "mimo-v2-6-distill-qwen-9b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 30 of 30, Ollama's library build at q4_K_M is 6.6 GB, 7.9 GB with a fifth added, while Xiaomi's only build is BF16 at 18.85 GB. Runtimes 25, it keeps Qwen3.5-9B's architecture, which released vLLM, SGLang, Transformers and llama.cpp run, and Ollama's own library carries this model (entry updated 3 November 2026; the 21 October check found none). Builds 0, Xiaomi publishes no quantised build. Terms 15, MIT on the card over an Apache 2.0 base, both with nothing added. Download 10, not gated. Hosted 0, no provider serves this distill.",
          "b": "Memory 15, the only build is the safetensors set, 38.14 GB with its MTP head, since the Hub counts 9.41B of its parameters in F32. Runtimes 0, none of the six runs it in a released version. Builds 0, SenseNova publishes one set of weights and no quantised build. Terms 15, Apache 2.0 in the card's metadata and on the card, which links the LICENSE in the GitHub repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 15 + 0 + 0 + 15 + 10 + 0 = 40."
        },
        "scores": {
          "a": 80,
          "b": 40
        }
      },
      {
        "by": 15,
        "category": "risk",
        "leads": "mimo-v2-6-distill-qwen-9b",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors only and no repository code runs. Provenance 15, the files are in Xiaomi's MiMo organisation, which mimo.xiaomi.com links to, with a checksum on every file. Safety evaluation 0, the technical report has no safety results. Agent guidance 5, the card's Use Restrictions section (28 October 2026) gives general cautions, with nothing on prompt injection, refusals or safeguards. Licence risk 15, MIT and Apache 2.0 with no conditions, and the card says its restrictions add none. Security programme 5 of 10, Xiaomi's company security centre takes reports, but nothing links it to MiMo and no advisories were found.",
          "b": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the sensenova organisation, which the GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on instructions inside tool results or images. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, SenseTime's about page gives a security contact (5). 15 + 15 + 0 + 0 + 15 + 5 = 50."
        },
        "scores": {
          "a": 65,
          "b": 50
        }
      },
      {
        "by": 10,
        "category": "interface",
        "leads": "mimo-v2-6-distill-qwen-9b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format is only in the template and the card names no tool parser. Trained tool calling 20, several calls a turn and agentic fine-tuning. Structured output 0, not documented. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 262,144 tokens from the base's native length, though the card doesn't say.",
          "b": "Chat template 20, chat_template.jinja in the repository (7,756 characters). Tool-calling format 10, only the template says it: a tools block, and calls written as tool_call blocks with function and parameter tags. Trained tool calling 10, the card says the training recipes include tool trajectories and that the model is for agentic workflows. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the card documents enable_thinking in the chat template call, on or off per request, and the template puts the reasoning in think tags ahead of the answer. Context 10, 262,144 tokens in training, per the card's specification table, and config.json gives the language model 262,144 positions. 20 + 10 + 10 + 0 + 10 + 10 = 60."
        },
        "scores": {
          "a": 70,
          "b": 60
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Xiaomi (MiMo team)",
        "b": "SenseTime (SenseNova)",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "MIT (metadata only, no LICENSE file); built on Qwen3.5-9B under Apache 2.0",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-09-21",
        "b": "2026-11-02",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "not read yet",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "2.3/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "MiMo-V2.6 Distill Qwen 9B scores 61 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 3 of 4 scored categories.",
        "question": "Which is better for AI agents, MiMo-V2.6 Distill Qwen 9B or SenseNova-Loop-9B-Preview?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 80 against 40",
          "Risk, 65 against 50",
          "Agent interface, 70 against 60"
        ],
        "also": null,
        "goodFor": "Researchers and agents that want a small coding and terminal-agent model to fine-tune further on a 24 GB GPU, or to run at 4-bit through Ollama in about 8 GB.",
        "slug": "mimo-v2-6-distill-qwen-9b",
        "watchFor": "Only BF16 from Xiaomi, about 23 GB to run; the smaller builds are Ollama's"
      },
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today",
        "slug": "sensenova-loop-9b-preview",
        "watchFor": "No released runtime runs it. vLLM and SGLang need unmerged pull requests"
      }
    ],
    "history": {
      "mimo-v2-6-distill-qwen-9b": [
        {
          "causes": [
            "product"
          ],
          "date": "2026-11-03",
          "delta": 0,
          "from": 61,
          "fromGrade": "C",
          "to": 61,
          "toGrade": "C"
        }
      ],
      "sensenova-loop-9b-preview": null
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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      {
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      {
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-mimo-v2-6-distill-qwen-9b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-mimo-v2-6-distill-qwen-9b"
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-mimo-v2-6-distill-qwen-9b.json",
        "title": "GLM-5.3 vs MiMo-V2.6 Distill Qwen 9B",
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-sensenova-loop-9b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-mimo-v2-6-distill-qwen-9b.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-mimo-v2-6-distill-qwen-9b"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-mimo-v2-6-distill-qwen-9b.json",
        "title": "gpt-oss-20b vs MiMo-V2.6 Distill Qwen 9B",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-mimo-v2-6-distill-qwen-9b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mimo-v2-6-distill-qwen-9b.json",
        "title": "Granite 4.2 3B vs MiMo-V2.6 Distill Qwen 9B",
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-sensenova-loop-9b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-sensenova-loop-9b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-mimo-v2-6-distill-qwen-9b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-sensenova-loop-9b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-sensenova-loop-9b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-sensenova-loop-9b-preview.json",
        "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs SenseNova-Loop-9B-Preview",
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-sensenova-loop-9b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-sensenova-loop-9b-preview.json",
        "title": "North Mini Code 1.0 vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-sensenova-loop-9b-preview"
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        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-sensenova-loop-9b-preview.json",
        "title": "occamy-1.0 vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-sensenova-loop-9b-preview"
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        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-sensenova-loop-9b-preview.json",
        "title": "Olmo 3 7B Think vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-sensenova-loop-9b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-sensenova-loop-9b-preview.json",
        "title": "Ornith-1.5-122B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-sensenova-loop-9b-preview"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-sensenova-loop-9b-preview.json",
        "title": "Ornith-1.5-35B-A3B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-sensenova-loop-9b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-sensenova-loop-9b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-sensenova-loop-9b-preview"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-sensenova-loop-9b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-sensenova-loop-9b-preview.json",
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        "title": "Qwen-AgentWorld-35B-A3B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-sensenova-loop-9b-preview"
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        "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-sensenova-loop-9b-preview.json",
        "title": "Qwen3-30B-A3B-Instruct-2507 vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-sensenova-loop-9b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-sensenova-loop-9b-preview.json",
        "title": "Qwen3-4B-Instruct-2507 vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-sensenova-loop-9b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-sensenova-loop-9b-preview.json",
        "title": "Qwen3.5-0.8B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-sensenova-loop-9b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-sensenova-loop-9b-preview.json",
        "title": "Qwen3.5-122B-A10B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-sensenova-loop-9b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-sensenova-loop-9b-preview.json",
        "title": "Qwen3.5-2B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-sensenova-loop-9b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-sensenova-loop-9b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-sensenova-loop-9b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-sensenova-loop-9b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-sensenova-loop-9b-preview"
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        "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-sensenova-loop-9b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-sensenova-loop-9b-preview"
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        "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-sensenova-loop-9b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-sensenova-loop-9b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-sensenova-loop-9b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-sensenova-loop-9b-preview"
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        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-sensenova-loop-9b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-sensenova-loop-9b-preview.json",
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      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-sensenova-loop-9b-preview.json",
        "title": "Recursive Flint vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-sensenova-loop-9b-preview.json",
        "title": "Ripple-65B Preview vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-sensenova-loop-9b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-smollm3-3b.json",
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        "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-smollm3-3b"
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        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-spark-x2-6-4b.json",
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        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-step-3-7-flash"
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        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-unlimited-ocr-2.json",
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        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b.json",
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        "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b.json",
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    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 40,
        "edge": "mimo-v2-6-distill-qwen-9b",
        "key": "reach",
        "mimo-v2-6-distill-qwen-9b": 80,
        "name": "Reach",
        "sensenova-loop-9b-preview": 40,
        "weight": 25
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      {
        "by": 15,
        "edge": "mimo-v2-6-distill-qwen-9b",
        "key": "risk",
        "mimo-v2-6-distill-qwen-9b": 65,
        "name": "Risk",
        "sensenova-loop-9b-preview": 50,
        "weight": 20
      },
      {
        "by": 10,
        "edge": "mimo-v2-6-distill-qwen-9b",
        "key": "interface",
        "mimo-v2-6-distill-qwen-9b": 70,
        "name": "Agent interface",
        "sensenova-loop-9b-preview": 60,
        "weight": 10
      },
      {
        "by": 4,
        "edge": "sensenova-loop-9b-preview",
        "key": "upkeep",
        "mimo-v2-6-distill-qwen-9b": 40,
        "name": "Documentation and upkeep",
        "sensenova-loop-9b-preview": 44,
        "weight": 10
      }
    ],
    "summary": "MiMo-V2.6 Distill Qwen 9B scores 61 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "mimo-v2-6-distill-qwen-9b": "A 9B cousin tuned on MiMo-V2.6's agent work for coding, terminal tasks and visual coding, released as a base for RL research. Ollama's library build needs about 8 GB at 4-bit, while Xiaomi ships only BF16. The main risks are no safety results while a seventh of its tuning is offensive security, and nobody at Xiaomi answering reports.",
      "sensenova-loop-9b-preview": "An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
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  },
  "markdown": "MiMo-V2.6 Distill Qwen 9B scores 61 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.\n\n- MiMo-V2.6 Distill Qwen 9B: grade C, 61/100, rank #47 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b.md · JSON https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-distill-qwen-9b.json\n- SenseNova-Loop-9B-Preview: grade D, 46.8/100, rank #155 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### MiMo-V2.6 Distill Qwen 9B (C)\n\nGood for: Researchers and agents that want a small coding and terminal-agent model to fine-tune further on a 24 GB GPU, or to run at 4-bit through Ollama in about 8 GB.\n\nAhead on:\n- Reach, 80 against 40\n- Risk, 65 against 50\n- Agent interface, 70 against 60\n\nWatch for: Only BF16 from Xiaomi, about 23 GB to run; the smaller builds are Ollama's\n\n### SenseNova-Loop-9B-Preview (D)\n\nGood for: Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today\n\nWatch for: No released runtime runs it. vLLM and SGLang need unmerged pull requests\n\n\n## Why the edge\n\n### Reach: MiMo-V2.6 Distill Qwen 9B +40\n\n- MiMo-V2.6 Distill Qwen 9B (80): Memory 30 of 30, Ollama's library build at q4_K_M is 6.6 GB, 7.9 GB with a fifth added, while Xiaomi's only build is BF16 at 18.85 GB. Runtimes 25, it keeps Qwen3.5-9B's architecture, which released vLLM, SGLang, Transformers and llama.cpp run, and Ollama's own library carries this model (entry updated 3 November 2026; the 21 October check found none). Builds 0, Xiaomi publishes no quantised build. Terms 15, MIT on the card over an Apache 2.0 base, both with nothing added. Download 10, not gated. Hosted 0, no provider serves this distill.\n- SenseNova-Loop-9B-Preview (40): Memory 15, the only build is the safetensors set, 38.14 GB with its MTP head, since the Hub counts 9.41B of its parameters in F32. Runtimes 0, none of the six runs it in a released version. Builds 0, SenseNova publishes one set of weights and no quantised build. Terms 15, Apache 2.0 in the card's metadata and on the card, which links the LICENSE in the GitHub repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 15 + 0 + 0 + 15 + 10 + 0 = 40.\n\n### Risk: MiMo-V2.6 Distill Qwen 9B +15\n\n- MiMo-V2.6 Distill Qwen 9B (65): File safety 25, safetensors only and no repository code runs. Provenance 15, the files are in Xiaomi's MiMo organisation, which mimo.xiaomi.com links to, with a checksum on every file. Safety evaluation 0, the technical report has no safety results. Agent guidance 5, the card's Use Restrictions section (28 October 2026) gives general cautions, with nothing on prompt injection, refusals or safeguards. Licence risk 15, MIT and Apache 2.0 with no conditions, and the card says its restrictions add none. Security programme 5 of 10, Xiaomi's company security centre takes reports, but nothing links it to MiMo and no advisories were found.\n- SenseNova-Loop-9B-Preview (50): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the sensenova organisation, which the GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on instructions inside tool results or images. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, SenseTime's about page gives a security contact (5). 15 + 15 + 0 + 0 + 15 + 5 = 50.\n\n### Agent interface: MiMo-V2.6 Distill Qwen 9B +10\n\n- MiMo-V2.6 Distill Qwen 9B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format is only in the template and the card names no tool parser. Trained tool calling 20, several calls a turn and agentic fine-tuning. Structured output 0, not documented. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 262,144 tokens from the base's native length, though the card doesn't say.\n- SenseNova-Loop-9B-Preview (60): Chat template 20, chat_template.jinja in the repository (7,756 characters). Tool-calling format 10, only the template says it: a tools block, and calls written as tool_call blocks with function and parameter tags. Trained tool calling 10, the card says the training recipes include tool trajectories and that the model is for agentic workflows. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the card documents enable_thinking in the chat template call, on or off per request, and the template puts the reasoning in think tags ahead of the answer. Context 10, 262,144 tokens in training, per the card's specification table, and config.json gives the language model 262,144 positions. 20 + 10 + 10 + 0 + 10 + 10 = 60.\n\n## Score by category\n\n| Category | Weight | MiMo-V2.6 Distill Qwen 9B | SenseNova-Loop-9B-Preview | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 80 | 40 | MiMo-V2.6 Distill Qwen 9B +40 |\n| Risk | 20% (20 this run) | 65 | 50 | MiMo-V2.6 Distill Qwen 9B +15 |\n| Agent interface | 10% (10 this run) | 70 | 60 | MiMo-V2.6 Distill Qwen 9B +10 |\n| Documentation and upkeep | 10% (10 this run) | 40 | 44 | SenseNova-Loop-9B-Preview +4 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **46.8 · D** | |\n\n## Facts side by side\n\n| Fact | MiMo-V2.6 Distill Qwen 9B | SenseNova-Loop-9B-Preview |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Xiaomi (MiMo team) | SenseTime (SenseNova) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT (metadata only, no LICENSE file); built on Qwen3.5-9B under Apache 2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-09-21 | 2026-11-02 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.3/5 (4) | none |\n\n## How the scores have moved\n\n### MiMo-V2.6 Distill Qwen 9B\n\n- no change on 3 November 2026: product\n\n### SenseNova-Loop-9B-Preview\n\nNo score change since it was listed.\n\n## Verdicts\n\n**MiMo-V2.6 Distill Qwen 9B.** A 9B cousin tuned on MiMo-V2.6's agent work for coding, terminal tasks and visual coding, released as a base for RL research. Ollama's library build needs about 8 GB at 4-bit, while Xiaomi ships only BF16. The main risks are no safety results while a seventh of its tuning is offensive security, and nobody at Xiaomi answering reports.\n\n**SenseNova-Loop-9B-Preview.** An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet.\n\n## Before you call either\n\n### MiMo-V2.6 Distill Qwen 9B\n\n1. Quickest start is `ollama run mimo-v2.6:9b`, Ollama's 6.6 GB q4_K_M build with text and image input\n2. Serve it as the card does, `sglang serve --model-path XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B --reasoning-parser mimo`, and add `--tool-call-parser mimo` for parsed tool calls\n3. SGLang's mimo reasoning parser expects thinking to be asked for, so pass `chat_template_kwargs` with `enable_thinking` true, as the card does, and read `reasoning_content`\n4. Calls come back as function and parameter blocks inside tool_call, the same shape vLLM's and SGLang's qwen3_coder parsers read\n5. The generation config samples at temperature 0.6, top_k 20 and top_p 0.95\n6. Images and video go in through Qwen3.5's processor; the template's audio tokens have no audio encoder behind them\n7. The MTP head isn't in the files, so skip MTP speculative decoding even though config.json lists a layer\n8. It's an SFT checkpoint meant for further RL, and discussions report runaway generation on llama.cpp, so cap max_tokens\n9. If you redistribute the weights, include the Apache 2.0 text of Qwen3.5-9B, which section 4(a) asks for and the repository lacks\n\n### SenseNova-Loop-9B-Preview\n\n1. Read modeling_sensenova_loop.py before you load it. trust_remote_code runs it\n2. Pin transformers 5.18.0, the version the card checked\n3. Load with dtype=torch.bfloat16 as the card does, or the 38 GB of F32 weights stay at full size\n4. Pass enable_thinking=False to apply_chat_template for a direct answer\n5. Tool calls come back as tool_call blocks with function and parameter tags. Parse them yourself, since no runtime ships a parser\n6. Wait for the vLLM or SGLang pull request to land in a release before putting it behind an API\n\n## Questions\n\n### Which is better for AI agents, MiMo-V2.6 Distill Qwen 9B or SenseNova-Loop-9B-Preview?\n\nMiMo-V2.6 Distill Qwen 9B scores 61 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-sensenova-loop-9b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-sensenova-loop-9b-preview.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"mimo-v2-6-distill-qwen-9b\", \"b\": \"sensenova-loop-9b-preview\"}`. 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